council

Name: council
Availability: InStock
Author: zackham

Query frontier AI models via OpenRouter and synthesize consensus responses.

Updated Feb 25, 2026

Search Tags:#decision making #ai #llm #multi-model #consensus #synthesis

Ready to run

npx skills add https://github.com/zackham/agent-skills --skill council-zackham

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: council
Source: https://github.com/zackham/agent-skills/tree/main/council
Command: npx skills add https://github.com/zackham/agent-skills --skill council-zackham

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires httpx, and includes scripts (resource) components.

What problem does it solve?

This Skill provides a robust method for achieving multi-model consensus on complex questions, mitigating individual model biases and blindspots by synthesizing diverse AI perspectives.

Core Features & Use Cases

Triangulated Answers: Obtains answers from multiple frontier AI models (Claude Opus, GPT-5.4, Gemini Pro, Grok) simultaneously.
Synthesized Output: Consolidates responses into a single, coherent answer, highlighting areas of agreement and disagreement.
Contextual Enrichment: Optionally gathers relevant context from your codebase or documentation to inform model queries.
Use Case: When deciding on a critical architectural choice, use the council skill to get a balanced view from different AI models, ensuring all angles are considered before making a final decision.

Quick Start

Ask the council about the best approach for implementing a new caching layer in our application.

Frequently Asked Questions about council

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I get multi-model consensus from different AI models for a complex decision?▼

Multi-model consensus is achieved by querying frontier AI models via OpenRouter simultaneously and synthesizing their responses to mitigate individual model biases. This approach triangulates answers to highlight areas of agreement and disagreement.

How does multi-model synthesis reduce single-model bias in architectural decision-making?▼

Multi-model synthesis reduces single-model bias by obtaining simultaneous answers from diverse frontier models like Claude Opus, GPT-5.4, Gemini Pro, and Grok. It consolidates these diverse AI perspectives into a single coherent output, balancing blindspots.

Can I use codebase context to inform multi-model AI queries?▼

You can use codebase context to inform multi-model AI queries through optional context gathering. This enriches the query framing by gathering relevant context from your documentation before querying the frontier models.

Do I need httpx to query frontier AI models via OpenRouter?▼

You need httpx to query frontier AI models via OpenRouter for multi-model consensus. The httpx dependency handles the required API communication to fetch and synthesize responses from the different models.

What is the best way to compare answers from multiple LLMs?▼

The best way to compare answers from multiple LLMs is using a multi-model consensus approach that queries different models and synthesizes their responses. This highlights areas of agreement and disagreement for tough decisions.

When should I not use multi-model consensus for decision making?▼

You should not use multi-model consensus for simple decisions that do not require triangulated answers from multiple frontier AI models. It is designed for complex decision-making scenarios where mitigating single-model bias is critical.